from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)





def 			lowercase(	UpperCamelCase_       )		->	List[List[ImageInput]]:







 '''simple docstring'''
 if isinstance(UpperCamelCase_							,					(list, tuple)       ) and isinstance(videos[0]							,					(list, tuple)       ) and is_valid_image(videos[0][0]       ):
  return videos

 elif isinstance(UpperCamelCase_							,					(list, tuple)       ) and is_valid_image(videos[0]       ):
  return [videos]

 elif is_valid_image(UpperCamelCase_       ):
  return [[videos]]

 raise ValueError(f"""Could not make batched video from {videos}"""       )


class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""pixel_values"""]







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						PILImageResampling = PILImageResampling.BILINEAR   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Union[int, float] = 1 / 255   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[float, List[float]]] = None   ,   lowerCamelCase_						:						Optional[Union[float, List[float]]] = None   ,   **lowerCamelCase_						:						Union[str, Any]   ,   ):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      size if size is not None else {"""shortest_edge""": 256}
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
    UpperCamelCase			  =      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   param_name="""crop_size"""				)

    UpperCamelCase			  =      do_resize
    UpperCamelCase			  =      size
    UpperCamelCase			  =      do_center_crop
    UpperCamelCase			  =      crop_size
    UpperCamelCase			  =      resample
    UpperCamelCase			  =      do_rescale
    UpperCamelCase			  =      rescale_factor
    UpperCamelCase			  =      offset
    UpperCamelCase			  =      do_normalize
    UpperCamelCase			  =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    UpperCamelCase			  =      image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Dict[str, int]   ,   lowerCamelCase_						:						PILImageResampling = PILImageResampling.BILINEAR   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						Any   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
    if "shortest_edge" in size:
     UpperCamelCase			  =      get_resize_output_image_size(lowerCamelCase_   ,   size["""shortest_edge"""]   ,   default_to_square=lowerCamelCase_				)
    elif "height" in size and "width" in size:
     UpperCamelCase			  =      (size["""height"""], size["""width"""])
    else:
     raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
    return resize(lowerCamelCase_   ,   size=lowerCamelCase_   ,   resample=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Dict[str, int]   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      get_size_dict(lowerCamelCase_				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
    return center_crop(lowerCamelCase_   ,   size=(size["""height"""], size["""width"""])   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Union[int, float]   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      image.astype(np.floataa				)
    if offset:
     UpperCamelCase			  =      image - (scale / 2)
    return rescale(lowerCamelCase_   ,   scale=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Union[float, List[float]]   ,   lowerCamelCase_						:						Union[float, List[float]]   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						List[str]   ,   ):





    """simple docstring"""


    return normalize(lowerCamelCase_   ,   mean=lowerCamelCase_   ,   std=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						ImageInput   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						PILImageResampling = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						float = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Optional[Union[float, List[float]]] = None   ,   lowerCamelCase_						:						Optional[Union[float, List[float]]] = None   ,   lowerCamelCase_						:						Optional[ChannelDimension] = ChannelDimension.FIRST   ,   ):





    """simple docstring"""


    if do_resize and size is None or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True."""				)

    if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

    if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

    if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

    if offset and not do_rescale:
     raise ValueError("""For offset, do_rescale must also be set to True."""				)

    # All transformations expect numpy arrays.
    UpperCamelCase			  =      to_numpy_array(lowerCamelCase_				)

    if do_resize:
     UpperCamelCase			  =      self.resize(image=lowerCamelCase_   ,   size=lowerCamelCase_   ,   resample=lowerCamelCase_				)

    if do_center_crop:
     UpperCamelCase			  =      self.center_crop(lowerCamelCase_   ,   size=lowerCamelCase_				)

    if do_rescale:
     UpperCamelCase			  =      self.rescale(image=lowerCamelCase_   ,   scale=lowerCamelCase_   ,   offset=lowerCamelCase_				)

    if do_normalize:
     UpperCamelCase			  =      self.normalize(image=lowerCamelCase_   ,   mean=lowerCamelCase_   ,   std=lowerCamelCase_				)

    UpperCamelCase			  =      to_channel_dimension_format(lowerCamelCase_   ,   lowerCamelCase_				)
    return image







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						ImageInput   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						PILImageResampling = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						float = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Optional[Union[float, List[float]]] = None   ,   lowerCamelCase_						:						Optional[Union[float, List[float]]] = None   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   lowerCamelCase_						:						ChannelDimension = ChannelDimension.FIRST   ,   **lowerCamelCase_						:						Tuple   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      do_resize if do_resize is not None else self.do_resize
    UpperCamelCase			  =      resample if resample is not None else self.resample
    UpperCamelCase			  =      do_center_crop if do_center_crop is not None else self.do_center_crop
    UpperCamelCase			  =      do_rescale if do_rescale is not None else self.do_rescale
    UpperCamelCase			  =      rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCamelCase			  =      offset if offset is not None else self.offset
    UpperCamelCase			  =      do_normalize if do_normalize is not None else self.do_normalize
    UpperCamelCase			  =      image_mean if image_mean is not None else self.image_mean
    UpperCamelCase			  =      image_std if image_std is not None else self.image_std

    UpperCamelCase			  =      size if size is not None else self.size
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   default_to_square=lowerCamelCase_				)
    UpperCamelCase			  =      crop_size if crop_size is not None else self.crop_size
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_   ,   param_name="""crop_size"""				)

    if not valid_images(lowerCamelCase_				):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""				)

    UpperCamelCase			  =      make_batched(lowerCamelCase_				)

    UpperCamelCase			  =      [
        [
            self._preprocess_image(
                image=lowerCamelCase_   ,   do_resize=lowerCamelCase_   ,   size=lowerCamelCase_   ,   resample=lowerCamelCase_   ,   do_center_crop=lowerCamelCase_   ,   crop_size=lowerCamelCase_   ,   do_rescale=lowerCamelCase_   ,   rescale_factor=lowerCamelCase_   ,   offset=lowerCamelCase_   ,   do_normalize=lowerCamelCase_   ,   image_mean=lowerCamelCase_   ,   image_std=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   )
            for img in video
        ]
        for video in videos
    ]

    UpperCamelCase			  =      {"""pixel_values""": videos}
    return BatchFeature(data=lowerCamelCase_   ,   tensor_type=lowerCamelCase_				)

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
